Add files using upload-large-folder tool

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README.md

@@ -0,0 +1,118 @@
+---
+license: cc-by-nc-4.0
+language:
+  - en
+  - zh
+pretty_name: SONAR
+task_categories: [audio-classification]
+size_categories: [1K<n<10K]
+configs:
+  - config_name: default
+    data_files:
+      - {split: test, path: "data/test-*.parquet"}
+tags:
+  - anti-spoofing
+  - audio-deepfake-detection
+  - speech
+  - benchmark
+  - arena-ready
+arxiv:
+  - "2410.04324"
+---
+
+# SONAR
+
+Benchmark-ready packaging of the **SONAR** synthetic-audio detection evaluation set
+([arXiv 2410.04324](https://arxiv.org/abs/2410.04324)), for speech anti-spoofing and
+synthetic / deepfake voice detection.
+
+## Overview
+
+SONAR is a balanced binary-classification benchmark: **bonafide** (genuine human speech)
+vs. **spoof** (AI-synthesized speech). The spoof side aggregates clips from **nine modern
+speech-synthesis systems**, deliberately spanning architectures and providers so that a
+detector cannot win by memorising one generator's artifacts. The bonafide side is an equal
+number of real clips sampled from the **LibriTTS** clean-test set. Original code and data:
+https://github.com/Jessegator/SONAR.
+
+| Side | Source | n |
+|------|--------|---|
+| bonafide | LibriTTS clean-test | 2274 |
+| spoof | OpenAI TTS | 600 |
+| spoof | xTTS | 600 |
+| spoof | Seed-TTS (en 300 + zh 300) | 600 |
+| spoof | FlashSpeech | 118 |
+| spoof | VoiceBox | 104 |
+| spoof | AudioGen | 100 |
+| spoof | VALL-E | 95 |
+| spoof | NaturalSpeech 3 | 32 |
+| spoof | PromptTTS 2 | 25 |
+
+## License & redistribution
+
+This **packaging** is released under the **Creative Commons Attribution-NonCommercial 4.0
+International (CC BY-NC 4.0)** license — see `LICENSE.txt`. SONAR is an aggregate of audio
+from multiple synthesis systems and from LibriTTS, each carrying its own upstream terms; the
+original authors note that the constituent sources "may be subject to different distribution
+licenses and usage restrictions." This repackaging is provided **for non-commercial research
+use only**; the underlying sources retain their own terms, and downstream users should
+consult the respective upstream providers' usage policies. Audio is re-encoded to 16 kHz mono
+FLAC for a uniform schema; labels are unmodified.
+
+## Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `path` | `string` | source-relative path (e.g. `OpenAI/alloy_69.wav`), unique |
+| `audio` | `Audio(16000)` | 16 kHz mono FLAC (re-encoded from heterogeneous source wav/mp3) |
+| `label` | `ClassLabel` | `"bonafide"` (0) / `"spoof"` (1) |
+| `notes` | `string` | JSON: `utterance_id`, `system`, `language` |
+
+`notes` example:
+```json
+{"utterance_id": "SONAR_OpenAI_alloy_69", "system": "OpenAI", "language": "en"}
+```
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("SpeechAntiSpoofingBenchmarks/SONAR", split="test")
+print(ds[0])
+```
+
+## Stats
+
+| Stat | Value |
+|------|-------|
+| Total trials | 4548 |
+| Bonafide | 2274 |
+| Spoof | 2274 |
+
+## Source provenance
+
+- Paper: https://arxiv.org/abs/2410.04324
+- Code / data: https://github.com/Jessegator/SONAR
+- Labels derived from the source directory layout (`real_samples/` = bonafide; the nine
+  synthesis-system directories = spoof).
+
+## Evaluation
+
+For evaluation instructions and submission format, see [`submissions/README.md`](submissions/README.md).
+
+## Citation
+
+```bibtex
+@article{li2024sonar,
+  title   = {Where are we in audio deepfake detection? A systematic analysis over
+             generative and detection models},
+  author  = {Li, Xiang and Chen, Pin-Yu and Wei, Wenqi},
+  journal = {arXiv preprint arXiv:2410.04324},
+  year    = {2024}
+}
+```
+
+## Maintainer
+
+Contact: k.n.borodin@mtuci.ru

build_parquet.py

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+"""Parquet build for the SONAR HF dataset repo.
+
+SONAR (arXiv 2410.04324) is a synthetic-audio detection benchmark: 2274 real
+clips (LibriTTS clean-test, in ``real_samples/``) plus 2274 fake clips from nine
+synthesis systems, each in its own top-level directory. There is **no protocol
+file** -- the label is the top-level directory (``real_samples`` = bonafide,
+every other dir = spoof). ``seedtts.csv`` is only the SeedTTS 600-file selection
+list and is already materialised on disk, so we ignore it and walk the tree.
+
+Always re-encode
+----------------
+The source is heterogeneous ``.wav``: sample rates 16 kHz **and** 24 kHz, subtypes
+PCM_16 / PCM_24 / FLOAT, and the ``OpenAI/`` dir is actually MP3 inside a ``.wav``
+name. A raw-byte embed would therefore ship non-16 kHz / non-PCM bytes that fail
+the validator's D3 / row-0 checks, so this builder ALWAYS re-encodes every clip
+with librosa -> clean 16 kHz mono FLAC into a staging dir (Stage 1), then
+assembles shards from the clean staging files (Stage 2). A whole-set soundfile
+decode probe still runs as a reporting gate. A non-silence guard rejects
+all-zero re-encodes (FLOAT-wav-read-as-int gotcha).
+
+Two stages, fully parallel, resumable. Sample mode (--limit N): first N rows into
+a single shard, skipping the full-count asserts (fast offline validate pass).
+"""
+
+import argparse
+import io
+import json
+import os
+import random
+import tempfile
+import warnings
+from concurrent.futures import ProcessPoolExecutor
+from pathlib import Path
+
+warnings.filterwarnings("ignore", message="PySoundFile failed")
+warnings.filterwarnings("ignore", category=FutureWarning, module="librosa")
+
+import datasets  # noqa: E402
+import librosa  # noqa: E402
+import numpy as np  # noqa: E402
+import soundfile as sf  # noqa: E402
+from datasets import Audio, ClassLabel, Dataset, Features, Value  # noqa: E402
+from tqdm.auto import tqdm  # noqa: E402
+
+try:
+    datasets.disable_progress_bars()
+except AttributeError:  # older datasets
+    from datasets.utils.logging import disable_progress_bar
+
+    disable_progress_bar()
+
+REPO_ROOT = Path(__file__).resolve().parent
+SRC_ROOT = Path("/home/kirill/mnt/users_4tb/datasets/sonar")
+REAL_DIR = "real_samples"  # bonafide; every other top-level dir is spoof
+PARQUET_DIR = REPO_ROOT / "data"
+STAGING_DIR = REPO_ROOT / "_clean_flac"
+NUM_SHARDS = 2
+EXPECTED_ROWS = 4548
+EXPECTED_BONAFIDE = 2274
+EXPECTED_SPOOF = 2274
+TARGET_SR = 16000
+PROBE_SAMPLE = 4548  # whole set
+WORKERS = int(os.environ.get("SONAR_BUILD_WORKERS", os.cpu_count() or 4))
+
+FEATURES = Features(
+    {
+        "path": Value("string"),
+        "audio": Audio(sampling_rate=16000),
+        "label": ClassLabel(names=["bonafide", "spoof"]),
+        "notes": Value("string"),
+    }
+)
+
+
+def _uid(relpath):
+    """Stable unique id from the relative path (the immutable join key)."""
+    stem = relpath[:-4] if relpath.lower().endswith(".wav") else relpath
+    return "SONAR_" + stem.replace("/", "_").replace(" ", "_")
+
+
+def build_catalogue():
+    """Walk SRC_ROOT for every .wav; label by top-level directory."""
+    records = []
+    for path in SRC_ROOT.rglob("*.wav"):
+        relpath = path.relative_to(SRC_ROOT).as_posix()
+        system = relpath.split("/", 1)[0]
+        label = "bonafide" if system == REAL_DIR else "spoof"
+        language = "zh" if "/zh/" in f"/{relpath}" else "en"
+        records.append(
+            {
+                "relpath": relpath,
+                "uid": _uid(relpath),
+                "system": system,
+                "language": language,
+                "label": label,
+            }
+        )
+    return records
+
+
+def build_notes(rec):
+    return json.dumps(
+        {
+            "utterance_id": rec["uid"],
+            "system": rec["system"],
+            "language": rec["language"],
+        }
+    )
+
+
+def _probe_one(relpath):
+    try:
+        data, _ = sf.read(str(SRC_ROOT / relpath))
+        if data.shape[0] == 0:
+            return f"{relpath}: empty"
+        return None
+    except Exception as e:  # noqa: BLE001
+        return f"{relpath}: {str(e).splitlines()[0][:100]}"
+
+
+def probe_decodability(records):
+    """Whole-set soundfile decode probe (reporting gate)."""
+    rng = random.Random(0)
+    sample = rng.sample(records, min(PROBE_SAMPLE, len(records)))
+    paths = [r["relpath"] for r in sample]
+    failures = []
+    with ProcessPoolExecutor(max_workers=WORKERS) as ex:
+        for err in ex.map(_probe_one, paths, chunksize=32):
+            if err:
+                failures.append(err)
+    print(f"Probe: {len(failures)}/{len(paths)} clips failed soundfile decode")
+    for f in failures[:10]:
+        print(f"  {f}")
+    return failures
+
+
+def _clip_duration(relpath):
+    y, _ = librosa.load(str(SRC_ROOT / relpath), sr=TARGET_SR, mono=True)
+    return len(y) / TARGET_SR
+
+
+def _ensure_long_first_row(records):
+    """Swap a clip with duration >= 1.0s to index 0 (validator D3 checks row 0)."""
+    for i in range(len(records)):
+        if _clip_duration(records[i]["relpath"]) >= 1.0:
+            if i != 0:
+                records[0], records[i] = records[i], records[0]
+            return
+    raise RuntimeError("No clip with duration >= 1.0s found")
+
+
+def _reencode_worker(task):
+    """Re-encode one source clip -> clean 16 kHz mono FLAC. Resumable + atomic."""
+    uid, relpath = task
+    dst = STAGING_DIR / f"{uid}.flac"
+    if dst.exists() and dst.stat().st_size > 0:
+        return (uid, None)
+    try:
+        y, _ = librosa.load(str(SRC_ROOT / relpath), sr=TARGET_SR, mono=True)
+        if y.size == 0:
+            return (uid, "empty after decode")
+        # Non-silence guard: a FLOAT wav misread as int yields all-zeros.
+        if not np.any(np.abs(y) > 1e-6):
+            return (uid, "all-silence after decode")
+        tmp = STAGING_DIR / f".{uid}.flac.tmp"
+        sf.write(str(tmp), y, TARGET_SR, format="FLAC")
+        os.replace(tmp, dst)
+        return (uid, None)
+    except Exception as e:  # noqa: BLE001
+        return (uid, str(e).splitlines()[0][:120])
+
+
+def stage1_reencode(records):
+    STAGING_DIR.mkdir(parents=True, exist_ok=True)
+    tasks = [(r["uid"], r["relpath"]) for r in records]
+    total = len(tasks)
+    print(f"Stage 1: re-encoding {total} files with {WORKERS} workers -> {STAGING_DIR}")
+    failures = []
+    with ProcessPoolExecutor(max_workers=WORKERS) as ex:
+        for uid, err in tqdm(
+            ex.map(_reencode_worker, tasks, chunksize=32),
+            total=total,
+            desc="Stage 1 re-encode",
+            unit="file",
+        ):
+            if err:
+                failures.append((uid, err))
+    print(f"Stage 1 done: {total} processed, {len(failures)} failures")
+    if failures:
+        for uid, err in failures[:10]:
+            print(f"  FAIL {uid}: {err}")
+        raise RuntimeError(f"{len(failures)} files failed to re-encode")
+
+
+def _build_shard(task):
+    """Worker: build one shard parquet from its row slice. Resumable + atomic."""
+    shard_index, rows, num_shards = task
+    shard_name = f"test-{shard_index:05d}-of-{num_shards:05d}.parquet"
+    final = PARQUET_DIR / shard_name
+    if final.exists() and final.stat().st_size > 0:
+        return (shard_index, len(rows), "skipped")
+
+    def row_gen():
+        for rec in rows:
+            uid = rec["uid"]
+            yield {
+                "path": rec["relpath"],
+                "audio": {
+                    "bytes": (STAGING_DIR / f"{uid}.flac").read_bytes(),
+                    "path": rec["relpath"],
+                },
+                "label": rec["label"],
+                "notes": build_notes(rec),
+            }
+
+    with tempfile.TemporaryDirectory() as cache:
+        ds = Dataset.from_generator(row_gen, features=FEATURES, cache_dir=cache)
+        tmp = PARQUET_DIR / f".{shard_name}.tmp"
+        ds.to_parquet(str(tmp))
+        os.replace(tmp, final)
+    return (shard_index, len(rows), "built")
+
+
+def _partition(records, num_shards):
+    n = len(records)
+    per = (n + num_shards - 1) // num_shards
+    out = []
+    for i in range(num_shards):
+        chunk = records[i * per : (i + 1) * per]
+        if chunk:
+            out.append(chunk)
+    return out
+
+
+def build():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--limit", type=int, default=None)
+    args = parser.parse_args()
+    limit = args.limit
+    sample_mode = limit is not None
+
+    print(f"Scanning {SRC_ROOT}")
+    records = build_catalogue()
+    print(f"Catalogued {len(records)} clips")
+    if not sample_mode:
+        assert len(records) == EXPECTED_ROWS, f"Expected {EXPECTED_ROWS}, got {len(records)}"
+
+    # uid is the immutable join key: sort by it for stable sharding.
+    records.sort(key=lambda r: r["uid"])
+    _ensure_long_first_row(records)
+
+    probe = records if not sample_mode else records[: max(limit * 4, limit)]
+    probe_decodability(probe)
+
+    if sample_mode:
+        records = records[:limit]
+        num_shards = 1
+        print(f"SAMPLE MODE: {len(records)} rows -> 1 shard")
+    else:
+        num_shards = NUM_SHARDS
+
+    bona = sum(1 for r in records if r["label"] == "bonafide")
+    spoof = sum(1 for r in records if r["label"] == "spoof")
+    print(f"  bonafide={bona} spoof={spoof} total={len(records)}")
+
+    stage1_reencode(records)
+
+    PARQUET_DIR.mkdir(parents=True, exist_ok=True)
+    # Drop stale shards whose total-count suffix differs from this run.
+    keep_suffix = f"-of-{num_shards:05d}.parquet"
+    for stale in PARQUET_DIR.glob("test-*.parquet"):
+        if not stale.name.endswith(keep_suffix):
+            print(f"Removing stale shard {stale.name}")
+            stale.unlink()
+
+    shards = _partition(records, num_shards)
+    tasks = [(i, rows, num_shards) for i, rows in enumerate(shards)]
+    stage2_workers = min(WORKERS, len(tasks))
+    print(f"Stage 2: building {len(tasks)} shard(s) with {stage2_workers} workers...")
+    built = skipped = 0
+    with ProcessPoolExecutor(max_workers=stage2_workers) as ex:
+        for idx, n, status in tqdm(
+            ex.map(_build_shard, tasks), total=len(tasks), desc="Stage 2 shards", unit="shard"
+        ):
+            if status == "built":
+                built += 1
+            elif status == "skipped":
+                skipped += 1
+    print(f"Stage 2 done: {built} built, {skipped} skipped")
+
+    _verify(num_shards, sample_mode)
+    print("All verifications passed!")
+
+    if not sample_mode:
+        from speech_spoof_bench import labels
+
+        out = labels.emit_labels(REPO_ROOT)
+        print(f"Wrote {out}")
+
+
+def _verify(num_shards, sample_mode):
+    import pyarrow.parquet as pq
+
+    shards = sorted(PARQUET_DIR.glob("test-*.parquet"))
+    total = sum(pq.read_metadata(str(f)).num_rows for f in shards)
+    uid_set, path_set, bona, spoof = set(), set(), 0, 0
+    for f in shards:
+        t = pq.read_table(str(f), columns=["path", "label", "notes"])
+        for p, lab, n in zip(
+            t.column("path").to_pylist(),
+            t.column("label").to_pylist(),
+            t.column("notes").to_pylist(),
+        ):
+            path_set.add(p)
+            uid_set.add(json.loads(n)["utterance_id"])
+            if lab == 0:
+                bona += 1
+            elif lab == 1:
+                spoof += 1
+    assert len(uid_set) == total, "Duplicate utterance_ids"
+    assert len(path_set) == total, "Duplicate paths"
+    if not sample_mode:
+        assert total == EXPECTED_ROWS, f"{total} != {EXPECTED_ROWS}"
+        assert bona == EXPECTED_BONAFIDE, f"bonafide {bona} != {EXPECTED_BONAFIDE}"
+        assert spoof == EXPECTED_SPOOF, f"spoof {spoof} != {EXPECTED_SPOOF}"
+    t0 = pq.read_table(str(shards[0]))
+    assert set(t0.column_names) == {"path", "audio", "label", "notes"}, t0.column_names
+    audio0 = t0.column("audio")[0].as_py()
+    data, sr = sf.read(io.BytesIO(audio0["bytes"]))
+    dur = len(data) / sr
+    assert sr == 16000, f"row0 sr {sr} != 16000"
+    assert dur >= 1.0, f"row0 dur {dur:.2f}s < 1.0s"
+    print(f"  verify: {total} rows, row0 {sr}Hz {dur:.2f}s decodable OK")
+
+
+if __name__ == "__main__":
+    build()

data/labels.parquet

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+oid sha256:9edb7158cc6241bed48edcff96d10dc85f3b977b911dc4cd26ae3144584cb7c6
+size 35013

data/test-00000-of-00002.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1b7ebe9290425834bf8fcd0e471806247c68fa0a36979372e6a7d7a0f08f750
+size 288064744

data/test-00001-of-00002.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fe7259b670680394ca2e1d4477ba41e2ac0b6bb940c61ef8014a1e1487002e32
+size 290017252

eval.yaml

@@ -0,0 +1,26 @@
+name: SONAR
+description: >
+  SONAR synthetic-audio detection benchmark (arXiv 2410.04324). Balanced binary
+  classification: 2274 bonafide (LibriTTS clean-test) vs. 2274 spoof aggregated
+  from nine speech-synthesis systems (OpenAI TTS, xTTS, Seed-TTS, FlashSpeech,
+  VoiceBox, AudioGen, VALL-E, NaturalSpeech 3, PromptTTS 2). EER computed over all
+  4548 utterances.
+evaluation_framework: inspect-ai
+
+tasks:
+  - id: antispoofing_eval
+    config: default
+    split: test
+
+    field_spec:
+      input: audio
+      target: label
+
+    solvers:
+      - name: speech_spoof_bench_solver
+
+    scorers:
+      - name: speech_spoof_scorer
+
+    metrics:
+      - eer_percent

submissions/README.md

@@ -0,0 +1,81 @@
+# Benchmark Submissions
+
+To submit a result, you'll upload two files (no git clone required):
+
+1. **`scores.txt`** to **your own HF model repo** under `.eval_results/SpeechAntiSpoofingBenchmarks/SONAR/scores.txt`.
+2. **`<your-slug>.yaml`** as a pull request to **this dataset repo** under `submissions/<your-slug>.yaml`.
+
+The YAML in this repo carries a pinned URL pointing at your `scores.txt`, plus its sha256. Scores files do not live in this repo.
+
+## Submitter workflow
+
+### 1. Generate `scores.txt` locally
+
+```bash
+speech-spoof-bench run \
+  --model-module <your_package>:<YourModelClass> \
+  --datasets SpeechAntiSpoofingBenchmarks/SONAR
+```
+
+Output: `results/SONAR/scores.txt` (one line per utterance, `<utterance_id> <score>`, higher = more bonafide).
+
+### 2. Upload `scores.txt` to your model repo
+
+```bash
+huggingface-cli upload <your-owner>/<your-model-repo> \
+  results/SONAR/scores.txt \
+  .eval_results/SpeechAntiSpoofingBenchmarks/SONAR/scores.txt \
+  --repo-type=model \
+  --commit-message="Add SONAR scores"
+```
+
+**Note the commit sha** the CLI prints — you'll need it in the next step.
+
+### 3. Fill in the submission YAML
+
+Copy `results_template.yaml` to `<your-slug>.yaml` and fill in every field. The two most important fields:
+
+- `artifact.scores_url`: the **pinned** URL to your uploaded scores file. Use the commit sha from step 2, not `main`:
+  ```
+  https://huggingface.co/<your-owner>/<your-model-repo>/resolve/<commit-sha-from-step-2>/.eval_results/SpeechAntiSpoofingBenchmarks/SONAR/scores.txt
+  ```
+  URLs with `/resolve/main/` are rejected because they're mutable.
+- `artifact.scores_sha256`: `sha256sum results/SONAR/scores.txt | awk '{print $1}'`.
+
+Leave the `reproduction:` block empty — the maintainer fills it in at merge time.
+
+### 4. Open the PR via HF CLI
+
+```bash
+huggingface-cli upload \
+  SpeechAntiSpoofingBenchmarks/SONAR \
+  <your-slug>.yaml submissions/<your-slug>.yaml \
+  --repo-type=dataset \
+  --create-pr \
+  --commit-message="Add <your-slug> submission"
+```
+
+The CLI prints a PR URL. That's it.
+
+### 5. Wait for maintainer reproduction
+
+A maintainer runs `speech-spoof-bench reproduce --scoring <PR-branch>`, which:
+- Fetches `scores_url`.
+- Verifies the sha256 against `artifact.scores_sha256`.
+- Recomputes EER from the file.
+- Compares to your claimed `scores.eer_percent` (must match within 1e-6).
+
+If it passes, the maintainer fills in `reproduction:` and merges. If it fails, you get a comment on the PR explaining why.
+
+## Verification levels
+
+| Level | What the maintainer checks | Cost |
+|---|---|---|
+| `scoring` (default) | sha + recomputed EER from your `scores.txt`. | Seconds. |
+| `inference` (optional, follow-up) | Re-runs your checkpoint end-to-end and regenerates `scores.txt`. Must match within 0.05% EER. | Expensive. |
+
+Submissions without a `reproduction:` block never appear in the arena.
+
+## What about git clone + push?
+
+You can do it that way too, but for a single 2 KB YAML it's massively heavier. The HF CLI path is the documented one.

submissions/results_template.yaml

@@ -0,0 +1,44 @@
+schema_version: 4
+
+system:
+  name: ""
+  slug: ""
+  description: ""
+  code: ""
+  checkpoint: ""
+  paper:
+    arxiv_id: ""
+    url: ""
+    bibtex: |
+      @article{...}
+
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/SONAR
+  revision: ""
+  split: test
+
+scores:
+  eer_percent: 0.0
+  n_trials: 4548
+  n_skipped: 0
+
+artifact:
+  # Must be pinned by commit sha. Pattern:
+  #   https://huggingface.co/<owner>/<repo>/resolve/<commit-sha>/.eval_results/SpeechAntiSpoofingBenchmarks/SONAR/scores.txt
+  scores_url: ""
+  scores_sha256: ""
+  bench_version: ""
+
+# Leave this block empty — the maintainer fills it in at merge.
+reproduction:
+  reproduced_by: ""
+  reproduced_at: ""
+  reproduced_bench_version: ""
+  match: ""
+
+submitter:
+  hf_username: ""
+  contact: ""
+
+submitted_at: ""
+notes: ""